Improving hydrodynamic modeling of river networks by incorporating data assimilation using a particle filter

作     者：Chenhui Jiang Dejun Zhu Haobo Li Xiaoqun Liu Danxun Li Chenhui Jiang;Dejun Zhu;Haobo Li;Xiaoqun Liu;Danxun Li

作者机构：State Key Laboratory of Hydroscience and Engineering Tsinghua University Hunan Institute of Water Resources and Hydropower Research 

出 版 物：《International Journal of Sediment Research》 (国际泥沙研究(英文版))

年 卷 期：2023年第38卷第5期

页      面：711-723页

核心收录：

学科分类：080103[工学-流体力学] 08[工学] 080104[工学-工程力学] 081502[工学-水力学及河流动力学] 0815[工学-水利工程] 0801[工学-力学（可授工学、理学学位）] 

基　　金：the financial support from the National Key R&D Program of China (Grant No. 2022YFC3201803) the National Natural Science Foundation of China (Grant No. 52179069) the Water Conservancy and Technology Program of Hunan Province, China (Grant Nos. XSKJ2019081-03 and XSKJ2021000-08) 

主　　题：River networks Hydrodynamic process Particle filter Likelihood function 

摘      要：Numerical modeling is a well-recognized method for studying the hydrodynamic processes in river networks. Multi-source measurements also offer abundant information on the patterns and mechanisms within the processes. Therefore, improving hydrodynamic modeling of river networks through the use of data assimilation techniques has become a hot research topic in recent years. The particle filter(PF) is a commonly used data assimilation method and has been proven to be applicable to various nonlinear and non-Gaussian models. In the current study, an improved numerical hydrodynamic model for large-scale river networks is established by incorporating the advanced PF algorithm. Furthermore, the PF method based on the Gaussian likelihood function(GLF) and the method based on the Cauchy likelihood function(CLF) are compared for a complex river network scenario. The feasibility of the PF-based methods was evaluated through application to the Yangtze-Dongting River-lake Network(YDRN) by assimilating water stage data collected at six hydrometric stations during the entire hydrodynamic process in ***, the parameters used in the likelihood function, which affect the assimilation performance,also were explored in the current study. The study results found that the accuracy of the model-derived water stage data was improved when the PF-based methods are utilized, with improvement not only at the data assimilation(calibration) sites but also at three hydrometric stations not used in the data assimilation(i.e., verification sites). The highest average Nash-Sutcliffe Efficiency result for the six assimilation sites were 0.98 while the lowest summed root-mean-square-error result was 1.801 m. The comparison results also indicated that the CLF-based PF outperformed the GLF-based PF when highaccuracy observed data are available. Specifically, the CLF can effectively resolve the filtering failure problem and the dispersion problem of PFs, and further improve the accuracy of th